Saranya G R, Viswanathan Pragasam
Renal Research Lab, Pearl Research Park, School of Bioscience and Technology, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India.
Saudi J Biol Sci. 2024 Aug;31(8):104028. doi: 10.1016/j.sjbs.2024.104028. Epub 2024 May 23.
Metabolites from the gut microbiota define molecules in the gut-kidney cross talks. However, the mechanistic pathway by which the kidneys actively sense gut metabolites and their impact on diabetic chronic kidney disease (DCKD) remains unclear. This study is an attempt to investigate the gut microbiome metabolites, their host targeting genes, and their mechanistic action against DCKD. Gut microbiome, metabolites, and host targets were extracted from the gutMgene database and metabolites from the PubChem database. DCKD targets were identified from DisGeNET, GeneCard, NCBI, and OMIM databases. Computational examination such as protein-protein interaction networks, enrichment pathway, identification of metabolites for potential targets using molecular docking, hubgene-microbes-metabolite-samplesource-substrate (HMMSS) network architecture were executed using Network analyst, ShinyGo, GeneMania, Cytoscape, Autodock tools. There were 574 microbial metabolites, 2861 DCKD targets, and 222 microbes targeting host genes. After screening, we obtained 27 final targets, which are used for computational examination. From enrichment analysis, we found NF-ΚB1, AKT1, EGFR, JUN, and RELA as the main regulators in the DCKD development through mitogen activated protein kinase (MAPK) pathway signalling. The (HMMSS) network analysis found and probiotic bacteria that are found in the intestinal epithelium, colonic region, metabolize the substrates like tryptophan, other unknown substrates might have direct interaction with the NF-kB1 and epidermal growth factor receptor (EGFR) targets. On docking of these target proteins with 3- Indole propionic acid (IPA) showed high binding energy affinity of -5.9 kcal/mol and -7.4kcal/mol. From this study we identified, the 3 IPA produced by was found to have renal sensing properties inhibiting MAPK/NF-KB1 inflammatory pathway and would be useful in treating CKD in diabetics.
肠道微生物群的代谢产物决定了肠道与肾脏相互作用中的分子。然而,肾脏主动感知肠道代谢产物的机制途径及其对糖尿病慢性肾脏病(DCKD)的影响仍不清楚。本研究旨在调查肠道微生物群代谢产物、其宿主靶向基因及其对DCKD的作用机制。从gutMgene数据库中提取肠道微生物群、代谢产物和宿主靶点,并从PubChem数据库中提取代谢产物。从DisGeNET、GeneCard、NCBI和OMIM数据库中识别DCKD靶点。使用Network analyst、ShinyGo、GeneMania、Cytoscape、Autodock工具进行蛋白质-蛋白质相互作用网络、富集途径、使用分子对接鉴定潜在靶点的代谢产物、中心基因-微生物-代谢产物-样本来源-底物(HMMSS)网络架构等计算分析。共有574种微生物代谢产物、2861个DCKD靶点和222种靶向宿主基因的微生物。筛选后,我们获得了27个最终靶点,用于计算分析。通过富集分析,我们发现NF-ΚB1、AKT1、EGFR、JUN和RELA是通过丝裂原活化蛋白激酶(MAPK)途径信号传导在DCKD发展中的主要调节因子。(HMMSS)网络分析发现,存在于肠道上皮、结肠区域的益生菌可代谢色氨酸等底物,其他未知底物可能与NF-kB1和表皮生长因子受体(EGFR)靶点直接相互作用。这些靶蛋白与3-吲哚丙酸(IPA)对接显示出-5.9 kcal/mol和-7.4kcal/mol的高结合能亲和力。通过本研究我们发现,由 产生的3 IPA具有肾脏感知特性,可抑制MAPK/NF-KB1炎症途径,对治疗糖尿病患者的CKD可能有用。
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